Systems and methods for identifying an acoustic source based on observed sound

ABSTRACT

An electronic device includes a processor, and a memory containing instructions that, when executed by the processor, cause the electronic device to learn a sound emitted by a legacy device and to issue an output when the electronic device subsequently hears the sound. For example, the electronic device can receive a training input and extract a compact representation of a sound in the training input, which the device stores. The device can receive an audio signal corresponding to an observed acoustic scene and extract a representation of the observed acoustic scene from the audio signal. The electronic device can determine whether the sound is present in the observed acoustic scene at least in part from a comparison of the representation of the observed acoustic scene with the representation of the sound. The electronic device emits a selected output responsive to determining that the sound is present in the acoustic scene.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.16/872,168, entitled “SYSTEMS AND METHODS FOR IDENTIFYING AN ACOUSTICSOURCE BASED ON OBSERVED SOUND,” filed May 11, 2020, which claims thebenefit of U.S. Provisional Application No. 62/874,389 entitled “SYSTEMSAND METHODS FOR IDENTIFYING AN ACOUSTIC SOURCE BASED ON OBSERVED SOUND,”filed Jul. 15, 2019, the entirety of which is incorporated herein byreference.

FIELD

This application and related subject matter (collectively referred to asthe “disclosure”) generally concern classifying acoustic scenes, andrelated systems and methods. More particularly, but not exclusively,this disclosure pertains to systems and methods for identifying anacoustic source based on observed sound.

BACKGROUND INFORMATION

Many home appliances, such as, for example, microwave ovens, washingmachines, dishwashers, and doorbells, make sounds to alert a user that acondition of the appliance has changed. However, users may be unable tohear an audible alert emitted by a home appliance for any of a varietyof reasons. For example, a user may have a hearing impairment, a usermay be outside or in another room, or the appliance may emit a soundobscured by a household acoustic scene.

Other areas, e.g., public spaces (government buildings), semi-publicspaces (office lobbies), and private spaces (residences or officebuildings), also have acoustic scenes that can contain sounds that carryinformation. For example, a bell, chime, or buzzer may indicate a doorhas been opened or closed, or an alarm may emit a siren or other soundalerting those nearby of a danger (e.g., smoke, fire, or carbonmonoxide).

SUMMARY

Some embodiments of disclosed electronic devices, processing modules,and associated methods, can learn a variety of sounds and can detectthose sounds when they occur or are otherwise present in an acousticscene. Moreover, some embodiments emit a signal or other outputresponsive to detecting a learned sound. Nonetheless, disclosedapproaches for detecting sounds do not require audio to be stored.Rather, a compact representation of observed audio can be stored, andextracted features from training audio can be compared to extractedfeatures representative of an observed acoustic scene. Consequently,disclosed approaches and systems can enhance user privacy compared toother approaches for recognizing sound. Further, by storing compactrepresentations of audio, the learning and detection processing can bestored locally on an electronic device, further enhancing privacy.(Embodiments having one or more tasks executed remotely, or in a cloudor other distributed network, also are contemplated by this disclosure.)

According to a first aspect, an electronic device includes a microphone,a processor and a memory. The memory contains instructions that, whenexecuted by the processor, cause the electronic device to receive atraining audio signal corresponding to a training input to themicrophone. The instructions further cause the electronic device toextract from the training audio signal a representation of a sound inthe training input and to store the representation of the sound. Theinstructions also cause the electronic device to receive an audio signalcorresponding to an acoustic scene observed by the microphone and toextract a representation of the observed acoustic scene from the audiosignal. As well, the instructions cause the electronic device todetermine whether the sound is present in the observed acoustic scene atleast in part from a comparison of the representation of the observedacoustic scene with the representation of the sound. The instructionsfurther cause the electronic device to emit a selected output responsiveto determining that the sound is present in the acoustic scene.

The electronic device can also receive a further training audio signalcorresponding to the sound and to update the stored representation ofthe sound in correspondence with the further training audio signal.

In some embodiments, the electronic device listens for the sound andupdates the stored representation of the sound when the devicedetermines the sound is present in an observed acoustic scene.

The training input can be a reference version of the sound and therepresentation of the sound can be a reference representation of thesound. The reference representation of the sound can correspond to acombination of the reference version of the sound and one or more of afrequency response representative of an environment in which theelectronic device operates, a background noise, and a combinationthereof. The reference representation of the sound can havereverberation or background impairments below a selected thresholdlevel. The reference representation of the sound can include informationpertaining to a direction from which the reference sound originates.

In some embodiments, the instructions further cause the electronicdevice, responsive to a user input, to record a training acoustic sceneand to define the reference representation of the sound based at leastin part on the recording of the training acoustic scene. In someembodiments, the instructions further cause the electronic device torequest from a user authorization to determine the referencerepresentation.

The reference version of the sound can have reverberation or backgroundimpairments below a selected threshold level and the referencerepresentation of the sound can be a first reference representation ofthe sound. The training audio signal can be a first training audiosignal and the instructions, when executed by the processor, can furthercause the electronic device to receive a plurality of other trainingaudio signals, each corresponding to a respective acoustic scene, and todefine a reference representation of the sound corresponding to each ofthe plurality of acoustic scenes. Each respective referencerepresentation of the sound can correspond to a combination of thereference version of the sound with the respective other audio signalcorresponding to the respective acoustic scene.

The instructions can further cause the electronic device to communicatethe classification to another electronic device or in a user-perceptiblemanner to a user, or both. The instructions can further cause the deviceto request from a user authorization to extract the representation ofthe sound in the training input.

The instructions can further cause the electronic device to assign therepresentation of the sound to a selected classification incorrespondence with the training input. For example, the sound can beassigned to a selected class of device, and the output can containinformation corresponding to the class of device.

The output can be a user-perceptible output or an output signaltransmitted to another device. A user-perceptible output can be one ormore of a visual output, a tactile output, an auditory output, anolfactory output, and a proprioceptive output.

According to another aspect, an electronic device includes a microphone,a processor, and a memory containing instructions that, when executed bythe processor, cause the electronic device to learn a sound that recursin an acoustic environment, to listen for and to detect a presence ofthe sound, and, responsive to a detected presence of the sound, to emitan output. For example, the sound can be emitted by another device. Theother device may be an analog device, an electronic device, or a devicehaving a combination of analog and digital components. After learningthe sound, the electronic device can listen for and detect the sound ina sound field observed by the microphone. Responsive to detecting thesound in the observed sound field, the electronic device can emit theoutput. The output can contain information that another device emittedthe sound.

The output can include a user-perceptible output. For example, theuser-perceptible output can include a visual output, a tactile output,an auditory output, an olfactory output, and a proprioceptive output.The instructions, when executed by the processor, can cause theelectronic device to condition one or more of the acts of learning thesound, listening for the sound, and detecting a presence of the sound onreceiving an input indicative of a user's authorization to perform theone or more acts. The electronic device can be configured to store arepresentation of the sound, and the instructions, when executed by theprocessor, can further cause the electronic device to update the storedrepresentation of the sound when the electronic device detects the soundin a sound field observed by the microphone.

In some embodiments, the instructions, when executed by the processor,can further cause the electronic device to prompt a user forauthorization to perform one or more of the acts of learning the sound,listening for the sound, and detecting a presence of the sound; and todiscern from a user-input whether the user has granted authorization toperform the one or more acts.

The instructions, when executed by the processor, can further cause theelectronic device to listen for the sound combined with one or moreother sounds corresponding to a selected acoustic scene. Theinstructions, when executed by the processor, can further cause theelectronic device to discern a source of the learned sound according toa direction from which the learned sound emanates.

According to another aspect, an electronic device includes a processor,and a memory containing instructions that, when executed by theprocessor, cause the electronic device to receive an audio signalcorresponding to an observed acoustic scene and to extract arepresentation of the observed acoustic scene from the audio signal. Forexample, the electronic device can define a reference representation ofsound received by the microphone from another device. The instructionsfurther cause the electronic device to compare the representation of theobserved acoustic scene to one or more representations of sound, e.g., areference representation of sound. Each representation of sound cancorrespond to a respective class of sound (e.g., a doorbell, amicrowave, a smoke alarm, etc.). The instructions also cause theelectronic device to determine whether one or more classes of sound ispresent the observed acoustic scene at least in part from a comparisonof the representation of the observed acoustic scene with each of theone or more representations of sound, and to emit a selected outputresponsive to determining that the sound class is present in theacoustic scene. For example, from the comparison, the electronic devicecan determine whether sound from another device is present in theobserved acoustic scene. The output emitted by the electronic device cancorrespond to the presence of sound from another device.

The selected output can be a user-perceptible output. The instructions,when executed by the processor, can further cause the electronic deviceto store the representation of the acoustic scene as a representation ofsound.

In some embodiments, the other device is a first device and thereference representation is a first reference representationcorresponding to the first device. The instructions, when executed bythe processor, can also cause the electronic device to define a secondreference representation of sound received by the microphone from asecond device. The electronic device can be configured to determinewhether sound from the second device is present in the observed acousticscene from a comparison of the representation of the observed acousticscene with the second reference representation. Responsive todetermining sound from the second device is present, the electronicdevice can emit a selected output corresponding to the presence of soundfrom the second device. In some embodiments, the audio signal is a firstaudio signal and the acoustic scene is a first acoustic scene. In somesuch embodiments, the instructions, when executed by the processor, canfurther cause the electronic device to receive a second audio signalcorresponding to a second observed acoustic scene and to extract arepresentation of the second observed acoustic scene from the secondaudio signal. The instructions can further cause the electronic deviceto determine whether the second acoustic scene contains a sound in thefirst acoustic scene based on a comparison of the representation of thesecond observed acoustic scene with the stored representation of thefirst acoustic scene.

According to still another aspect, an electronic device includes aprocessor, and a memory containing instructions that, when executed bythe processor, cause the electronic device to receive an inputcorresponding to a user input and, responsive to the input, to store areference representation of a sound. The instructions further cause theelectronic device to determine a compact representation of an observedacoustic scene and to determine whether the observed acoustic scenecontains the sound based in part on a comparison of the compactrepresentation of the observed acoustic scene with the referencerepresentation of the sound. The instructions also cause the electronicdevice to emit an output signal responsive to determining the observedacoustic scene contains the sound.

In some embodiments, the instructions, when executed by the processor,further cause the electronic device to receive a training inputcorresponding to the sound and, from the training input, to determinethe reference representation of the sound.

In some embodiments, the instructions, when executed by the processor,further cause the electronic device to receive an audio signalcorresponding to the observed acoustic scene, and to determine thecompact representation of the observed acoustic scene from the receivedaudio signal.

The output signal can be output over a communication connection withanother electronic device.

The instructions, when executed by the processor, can further cause theelectronic device to transmit, over a communication connection withanother electronic device, the reference representation of the sound.

Also disclosed are associated methods, as well as tangible,non-transitory computer-readable media including computer executableinstructions that, when executed, cause a computing environment toimplement one or more methods disclosed herein. Digital signalprocessors embodied in software, firmware, or hardware and beingsuitable for implementing such instructions also are disclosed.

The foregoing and other features and advantages will become moreapparent from the following detailed description, which proceeds withreference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring to the drawings, wherein like numerals refer to like partsthroughout the several views and this specification, aspects ofpresently disclosed principles are illustrated by way of example, andnot by way of limitation.

FIG. 1 illustrates a block diagram of a disclosed method for recognizingsound in an acoustic scene.

FIG. 2 illustrates an embodiment of a system configured to learn asound.

FIG. 3 illustrates an embodiment of a system configured to recognize asound.

FIG. 4 schematically illustrates a block diagram of an audio appliance.

FIG. 5 schematically illustrates an embodiment of an audio appliancecontaining several processing modules configured to carry out adisclosed method.

FIG. 6 illustrates a block diagram of a disclosed method for recognizingsound in an acoustic scene.

FIG. 7 illustrates a block diagram of a disclosed method for recognizingsound in an acoustic scene.

FIG. 8 illustrates a plot of first PCA elements for each of severalsources of sound.

FIG. 9 illustrates plots of first and second PCA elements for unitvectors representative of each of the sources depicted in FIG. 8projected onto a PCA space for one of the unit vectors.

FIG. 10 illustrates plots of third and fourth PCA elements for unitvectors representative of each of the sources depicted in FIG. 8projected onto a PCA space for one of the unit vectors.

FIG. 11 illustrates a comparison of projected values and cosine distancefor a first source of sound.

FIG. 12 illustrates a comparison of projected values and cosine distancefor a second source of sound.

FIG. 13 illustrates a comparison of projected values and cosine distancefor a third source of sound.

FIG. 14 illustrates a block diagram showing aspects of a computingenvironment.

DETAILED DESCRIPTION

The following describes various principles related to learning andrecognizing sounds, and related systems and methods. That said,descriptions herein of specific appliance, apparatus or systemconfigurations, and specific combinations of method acts, are butparticular examples of contemplated embodiments chosen as beingconvenient illustrative examples of disclosed principles. One or more ofthe disclosed principles can be incorporated in various otherembodiments to achieve any of a variety of corresponding, desiredcharacteristics. Thus, a person of ordinary skill in the art, followinga review of this disclosure, will appreciate that processing modules,electronic devices, and systems, having attributes that are differentfrom those specific examples discussed herein can embody one or morepresently disclosed principles, and can be used in applications notdescribed herein in detail. Such alternative embodiments also fallwithin the scope of this disclosure.

I. Overview

Sound carries a large amount of contextual information. Recognizingcommonly occurring sounds can allow electronic devices to adapt theirbehavior or to provide services responsive to an observed context (e.g.,as determined from observed sound), increasing their relevance and valueto users while requiring less assistance or input from the users.

FIG. 1 illustrates a method 10 for recognizing sound that some discloseddevices, appliances, devices, and systems can perform. The methodincludes learning to recognize recurrent sounds, at block 12. Once oneor more sounds are learned, the method includes listening for such oneor more sounds at block 13, and emitting an output responsive todetecting a learned sound, at block 14. One or more of the foregoingmethod acts may be conditioned on first receiving a user inputauthorizing such act(s), as indicated by the block 15.

Referring to FIGS. 2 and 3 , a device 100 equipped with a microphone canlearn to recognize a particular sound in the device's environment 110,then listen for and detect when the sound is present. Moreover, thedevice 100 can respond to the detection, for example, by sending anotification to a user's device 120 or otherwise emitting a signalcontemplated to alert a user of the sound. Further, some disclosedsystems can learn and detect sounds when subjected to acousticimpairments 130, such as, for example, noise and reverberation. Somedisclosed systems initiate training automatically, and some initiatetraining responsive to a user input. Further, some training informationcan be transferred from one device to another device, as indicated bythe bi-directional arrows between the devices 100 and 120 in FIG. 1 .

Stated differently, disclosed principles and embodiments thereof can addintelligence to a system that includes legacy (e.g., analog) appliancesand other devices by learning from emitted contextual sounds.

Further details of disclosed principles are set forth below. Section IIdescribes principles related to electronic devices, and Section IIIdescribes principles related to learning sounds. Section IV describesprinciples pertaining to extracting features from an audio signal andSection V describes principles concerning detecting previously learnedsounds within an observed acoustic scene. Section VI describesprinciples pertaining to output modules, e.g., suitable for emitting asignal responsive to detecting a learned sound. Section VII describesprinciples related to supervised learning and Section VIII describesprinciples pertaining to automated learning. and Section IX describesprinciples concerning detection of a direction from which a soundemanates. Section X describes principles pertaining to electronicdevices of the type that can embody presently disclosed principles, andA Section XI describes principles pertaining to computing environmentsof the type that can carry out disclosed methods or otherwise embodydisclosed principles. Section XII describes other embodiments ofdisclosed principles.

Other, related principles also are disclosed. For example, the followingdescribes machine-readable media containing instructions that, whenexecuted, cause a processor of, e.g., a computing environment, toperform one or more disclosed methods. Such instructions can be embeddedin software, firmware, or hardware. In addition, disclosed methods andtechniques can be carried out in a variety of forms of signal processor,again, in software, firmware, or hardware.

II. Electronic Devices

FIG. 4 shows an example of a suitable architecture for an audioappliance (e.g., electronic device 100 shown in FIGS. 2 and 3 ) that cancarry out one or more tasks related to learning and recognizing a soundcontained within a sound field (also sometimes referred to as anacoustic scene). The audio appliance 30 includes an audio acquisitionmodule 31 and aspects of a computing environment (e.g., described morefully below in connection with FIG. 14 ) that can cause the appliance torespond to an acoustic scene in a defined manner. For example, theillustrated appliance 30 includes a processing unit 34 and a memory 35that contains instructions the processing unit can execute to cause theaudio appliance to, e.g., carry out one or more aspects of acquiringsound from an acoustic scene, learning to recognize the acquired sound,and detecting the sound after it has been learned.

Such instructions can, for example, cause the audio appliance 30 tocapture sound with the audio acquisition module 31. The instructions cancause the audio appliance to invoke a learning task, e.g., to extract arepresentation of the captured sound. The learning task may be carriedout locally by the appliance 30 or by a remote computing system (notshown). The captured sound could include a sound emitted by anotherdevice, such as, for example, a washing machine or a doorbell.

Referring still to FIG. 4 , an audio appliance typically includes amicrophone transducer to convert incident acoustic signals tocorresponding electrical output. As used herein, the terms “microphone”and “microphone transducer” are used interchangeably and mean anacoustic-to-electric transducer or sensor that converts an incidentacoustic signal, or sound, into a corresponding electrical signalrepresentative of the incident acoustic signal. Typically, theelectrical signal output by the microphone is an analog signal.

Although a single microphone is depicted in FIG. 4 , the use of pluralmicrophones is contemplated by this disclosure. For example, pluralmicrophones can be used to obtain plural distinct acoustic signalsemanating from a given acoustic scene, and the plural versions can beprocessed independently and/or combined with one or more other versionsbefore further processing by the audio appliance 30. For example, abeamforming technique can combine outputs from plural microphones toestimate a direction from which a given sound arrived at the appliance.As well, or alternatively, the audio inputs from one or more microphoneson an external device may be provided to the audio appliance andcombined with, or compared to, the audio input(s) from each microphone(or a plurality of on-appliance microphones) prior to processing theaudio signals. Such processing can include, for example, determining adirection from which a sound originates, as through selected beamformingtechniques.

As shown in FIG. 4 , the audio acquisition module 31 can include amicrophone transducer 32 and a signal conditioner 33 to filter orotherwise condition the acquired audio signal. Some audio applianceshave an analog microphone transducer and a pre-amplifier to conditionthe signal from the microphone. Output from the pre-amplifier or otherconditioner can be filtered before being sampled by an analog-to-digitalconverter (ADC), though the output need not be filtered prior tosampling/digital conversion.

The appliance 30 may include an audio processing component 34. Forexample, as shown in FIG. 5 , the signal from the audio acquisitionblock 31 may be communicated to a processing module, e.g., a featureextraction module, a training module, a sound-detection module, anoutput module, and combinations thereof. Further each of the foregoingmodules (or any of them) may be local to a given appliance, remote fromthe appliance, or distributed between or among the appliance and one ormore other electronic devices.

Referring again to FIG. 4 , the memory 35 can store other instructionsthat, when executed by the processor 34, cause the audio appliance 30 toperform any of a variety of tasks such as, for example, tasks related tolearning sounds (e.g., block 40 in FIG. 5 ), detecting sounds (e.g.,block 45 in FIG. 5 ), alerting a user to detection of a sound (e.g.,block 49 in FIG. 5 ), and combinations thereof. As with tasks executedby a general computing environment, the aforementioned tasks can beexecuted locally to the device, remote from the device, or distributedbetween or among the appliance and one or more other electronic devices.For example, the audio appliance 30 schematically illustrated in FIG. 4includes a communication connection 36, as to establish and tofacilitate communication with another electronic device (e.g., acomputing environment).

An audio appliance can take the form of a portable media device, aportable communication device, a smart speaker, or any other electronicdevice. Audio appliances can be suitable for use with a variety ofaccessory devices. An accessory device can take the form of a wearabledevice, such as, for example, a smart-watch, an in-ear earbud, an on-earearphone, and an over-the-ear earphone. An accessory device can includeone or more electro-acoustic transducers or acoustic acquisition modulesas described above.

III. Training Module

Referring now to FIG. 5 , principles pertaining to training anelectronic device with a desired sound are described. In FIG. 5 ,selected principles are shown as being embodied in a training module 40.

The training module 40 receives an audio signal, e.g., from the audioacquisition module 31. During the training phase, the received audiosignal can be referred to as a training audio signal corresponding to atraining input. The training input can be any acoustic scene containinga target sound.

At block 41, the module 40 determines (e.g., locates) an onset of thetarget sound in an audio stream, and at block 42, the module trims thestream of audio data to discard information outside the frames thatcontain the target signal. The training module 40 (e.g., with theextraction module 43) extracts a representation of the target sound fromthe trimmed segment of the stream. At block 44, the module 40 saves theextracted representation as a reference representation.

Although FIG. 5 shows the training module as receiving the stream ofaudio from the audio acquisition module 31, the audio signal can bereceived by the electronic device from another electronic device as inFIG. 2 , e.g., over a communication connection (FIG. 1 ). The otherelectronic device can be any suitable appliance or device incommunication with the electronic device, such as, for example, aportable communication device, an audio appliance, an accessory device,or a smart-home device (e.g., a thermostat having a microphone).

In such an alternative embodiment, the other electronic device (e.g.,device 120) can receive sound from an acoustic environment to which thatdevice is exposed. The received sound can be designated as a traininginput. Output of an acoustic transducer (e.g., a microphone transducer)can be sampled to generate an audio signal. In the case of a traininginput, the sampling just described generates a training audio signal.The training audio signal can be communicated from the other electronicdevice (e.g., device 120) to the electronic device (e.g., appliance 100)contemplated to process audio signals to recognize one or more sounds inan acoustic scene.

Alternatively, the other electronic device can process the trainingaudio signal extract the reference representation, and the referencerepresentation can be communicated to the appliance.

Referring again to FIG. 5 , during a learning phase (also referred to asa “training phase”), a device can acquire an audio signal and aprocessing module (e.g., module 43) can extract features from the audiosignal that are suitable for identifying a sound to be learned. Theextracted components can be stored as a reference vector and theremaining components of the audio signal (e.g., noise, reverberation)can be discarded.

A learning mode can be invoked in several ways. For example, referringto FIG. 6 , a user can provide input to the device (e.g., in asupervised or semi-supervised learning mode) at block 51. In asupervised learning mode, a user can prompt the device to listen for asound to be learned. Alternatively the device can learn in asemi-supervised manner or in a fully autonomous manner (e.g., as with amethod 70 according to FIG. 7 ). In a semi-supervised mode, a device canrecognize recurrent sounds (e.g., sounds that may be relevant to a user)and verify that the sounds should be learned, e.g., by asking the userif the recurrent sounds should be learned. In an autonomous mode, thedevice can recognize recurrent sounds and automatically learn torecognize the recurrent sound without requiring an input, e.g.,verification, from a user. For example, the extraction module 43 (FIG. 5) can extract a compact representation of the sound, and a storagemodule 44 can store the representation (e.g., as also indicated at block52 in FIG. 6 .

To achieve a desirable user experience, some devices can learn a newsound based on a single, or just a few, examples of the sound. Further,some devices can detect a learned sound in the presence of acousticimpairments (e.g. background noise, reverberation).

Acoustic impairments can be accounted for when establishing a suitablethreshold by augmenting a recorded reference sound using amulti-condition training step when a device learns a new sound. Forexample, during training, the device can convolve the recorded soundwith a desired number of impulse responses (e.g., to account fordifferent levels of reverberation in an environment), and noise can beadded to create an augmented set of “recorded” sounds. Each “recorded”sound in the augmented set can be processed to generate a correspondingset of reference embeddings (or representations) of the “recorded”sounds, and a unit vector can be computed for each reference embeddingin the set.

Using such an approach, each reference embedding corresponds to arespective combination of impulse response and noise used to impair thebasic (or original) recorded reference sound. As well, augmenting oneclean example of a sound with a variety of impulse responses and noisespectra can broaden the training space without requiring a device torecord the underlying reference sound multiple times (e.g., underdifferent real conditions). Rather, such augmentation allows a device torecognize a given reference sound when present among a variety ofacoustic scenes.

Impairments (impulse responses and noise) can be preset (e.g., from afactory) or can be learned during use, e.g., from observations ofacoustic scenes to which the device is exposed during use. Additionally,reference sounds can be pre-recorded (e.g., during production) or thereference sounds can be learned during use (e.g., in a supervised,semi-supervised, or autonomous mode).

IV. Extraction Module

Additional details of processing modules configured to extract one ormore embeddings from an audio stream (e.g., an audio signal) are nowdescribed. As noted above briefly, the training module 40 (FIG. 5 ) caninclude or can invoke a task performed by an extraction module 43.Similarly, the detection module 45 (described more fully below) caninclude or can invoke a task performed by an extraction module 46. Insome systems, the same processing module provides the extractionfunction for the training module 40 and the detection module 45. Thefollowing principles can be embodied in any of the foregoing processingmodules configured to extract an embedding from an audio stream.

A neural network may be trained for a sound classification task andgenerate acoustic embeddings. With such a neural network, a sparse spacetypically separates sounds based on their individual acousticcharacteristics (e.g., spectral characteristics including, for example,pitch range, timbre, etc.) For example, embeddings of most sound classesother than a target class tend toward zero when projected onto asingle-class principle-components-analysis (PCA) space. Consequently,the direction of the unit vector in the PCA space corresponding to eachrespective class of sound differs from the directions of the other unitvectors. Accordingly, each class of sound can be discerned from othersounds.

In one embodiment, an audio signal can be transformed into atime-frequency representation, such as, for example, a log-Melspectrogram (or other low-level set of features). The sound can beprojected into a sparse space, e.g., an M-dimensional embedding, with aneural network (e.g., a VGG-type deep neural network) trained for asound-classification task. As noted, the sparse space can discriminatebetween or among different sounds based on their individual acousticcharacteristics.

When training a device to learn a new sound, the extraction module canprocess an audio signal containing the new sound, whether the audiosignal represents a reference version of the sound or an impairedversion of the sound. When determining whether a given acoustic scenecontains a target sound, the extraction module can process an audiosignal reflecting a recording of a given acoustic scene.

V. Detection Module

In a detection mode, an electronic device, e.g., the electronic device100 shown in FIG. 3 , using an extraction module as described above, canprocess an audio stream based on an observed acoustic scene to extractfeatures of the observed scene. The extracted features can be comparedto the reference features defined during training to assess whether alearned sound may be present in the observed acoustic scene. Forexample, if differences from the reference features are less than athreshold difference, the device can determine that it has detected aknown sound corresponding to the reference features.

Referring to FIGS. 5, 6 and 7 , tasks carried out by the detectionmodule 45 are described. The detection module 45 can continuouslytransform incoming audio into acoustic embedding frames. For example,the detection module 45 can invoke an extraction task (e.g., carried outby an extraction module 46), as indicated at block 53 or 63 (FIGS. 6 and7 ) to compute an observation unit vector for the incoming frame. Acomparison module 47 can compare the observation unit vector with one ormore reference unit vectors, as indicated at block 55 or 65 (FIGS. 6 and7 ). A decision module 48 can compare a difference between theobservation and the reference unit vectors to a threshold difference. Ifa threshold-difference parameter is satisfied, output module 49 of thedevice 30 can output a response, as indicated at block 57 or 67.

As noted, embeddings for many sounds may be sparse in a VGG-typesubspace. For example, almost 90% of embeddings in a 12 k VGG subspaceis a null space for most sounds. Accordingly, a 12 k subspace can bedown-sampled, e.g., to a 2 k space using a max-pooling technique intime. Such down-sampling can reduce dimensionality of the embedding thatotherwise could arise due to delays. And, as shown in FIG. 8 , forexample, the direction of the first principal component may be differentfor each of several reference sounds.

Effects of projecting sounds onto the direction of a target sound areshown for example in FIGS. 9 and 10 . To project sounds onto thedirection of the unit vector for the target sound, a dot product of theunit vector aligned with the target sound x/norm(x) with the unit vectorof another embedding (e.g., from an observed acoustic scene) can becomputed. Normalizing the other embedding as well as the targetembedding is similar or equivalent to computing the cosine distancebetween the unit vectors. As shown in FIGS. 9 and 10 , principalcomponents of other classes tend toward zero when projected tosingle-class PCA space. For example, as shown in FIGS. 9 and 10 , theunit vectors for the sound labeled “chicken” are shown scattered overthe first four principal components, and the unit vectors for a doorbelland a microwave are close to the origin of the “chicken” PCA spaceacross the first four principal components.

From the plots of the projected values and the cosine distance (FIGS. 11through 13 ), it can be seen that in general cosine distance has betterseparation. Also the cosine distance has a normalized scale of 0 to 1(compared to values in the 2K embeddings being nonnegative). The cosinedistance separates reasonably well for a rubber chicken sound (FIG. 11 )and clapping (FIG. 12 ).

However, co-sine distance does not separate as well for “Yup” sounds(FIG. 13 ). Clean sounds separate from non-target sounds, but targetwords with mixtures do not separate as well from the non-target sounds.That being said, disclosed principles pertain to discerning selectedclasses of environmental sounds other than speech and, as FIGS. 11 and12 show, disclosed principles can do that well for at least certainclasses of sound.

VI. Output Module

Once an underlying sound is learned, an output module 49 (FIG. 5 ) ofthe device 30 (FIG. 4 ) can send a notification to another device eachtime the device 30 detects (or “hears”) the sound in an observedacoustic scene. Thus, when a washing machine emits a tone indicating awash cycle has finished, a device that has learned to recognize thatsound can send a notification or otherwise emit an output, as to alert auser. In an example, the notification can include a message sent toanother device, and in another example, the output can cause one or moreroom lights to flash. As well, the output emitted by the output module49 may differ between sounds.

For example, when a doorbell rings, a disclosed audio appliance mayinstruct a controller to cause room lights to flash. When a washingmachine emits a tone indicating a wash cycle has concluded, the audioappliance may send a notification message to a user's accessory device(e.g., a smart phone or a smart watch) indicating that the wash cyclehas concluded. Additionally or alternatively, the output from the audioappliance may cause the accessory device to generate a haptic output.

Generally, a disclosed electronic device can emit an output using anysuitable form of output device. In an example, the output may be anoutput signal emitted over a communication connection as described morefully below in connection with general purpose computing environments.

VII. Supervised Learning Module

Some electronic devices invoke a supervised learning task, e.g., using asupervised learning module, responsive to a user input (or other inputindicative of a received user input). In general, a user can invoke asupervised learning mode before or after a target sound is emitted. Inone example, a user can provide an input to an electronic device afterhearing a desired sound, indicating that the device should learn arecent sound. Responsive to the user input, the electronic device caninvoke a training task as described above and process a buffered audiosignal (e.g., can “look back”) to extract an embedding of a recenttarget sound. In another example, and in response to a user input, adevice can listen prospectively for a target sound and can extract anembedding of an incoming audio signal. In an embodiment, the device canenter a listening mode responsive to receiving a user input, and once inthe listening mode the system can prompt the user to present the targetsound.

VIII. Automated Learning Module

Some electronic devices can invoke an automated learning task orautomated learning module. For example, an extraction module or task cancontinuously process incoming audio (e.g., captured in a circularbuffer), computing incoming unit vectors for an acoustic scene. Theautomated learning task can estimate a histogram or other measure ofsound occurrence from the incoming vectors. Once the estimated number ofoccurrences exceeds a threshold number of occurrences for a givenembedding, the automated learning module can store the embedding as acandidate reference embedding. On a subsequent embedding within athreshold difference of the candidate reference embedding, the devicecan prompt a user if the corresponding sound should be learned. Anaffirmative user response can cause the device to promote the candidatereference embedding to a reference embedding.

In other embodiments, a user is not prompted. For example, once theestimated number of occurrences exceeds a threshold number ofoccurrences for a given embedding, the automated learning module canstore the embedding as a new reference embedding. On a subsequentembedding within a threshold difference of the new reference embedding,the device can emit an output indicating that the newly learned soundhas been detected. In this type of embodiment, a can prompt the deviceto delete the new reference embedding, or can reclassify the underlyingsound if the device misclassified it originally.

IX. Orientation Module

Spatial cues can improve robustness of disclosed systems. In manyinstances, particularly for devices that are not intended to beportable, a sound to be learned might originate from a particulardirection. For example, a given smart speaker may be placed on abookshelf and a target sound may be associated with a microwave oven ora doorbell. If the electronic device (in this instance, the smartspeaker) is equipped with several microphones, beamforming techniquescan estimate a direction from which sound approaches the device. Stateddifferently, the direction of arrival (DOA) of incoming sounds can beestimated.

In some disclosed embodiments, the DOA can be used in addition toembeddings described above to define an M+1 sparse space, and the devicecan learn sounds not only based on their particular acousticcharacteristics but also based on the DOA.

In other embodiments, spatial cues can be used to generate anL-dimensional spatial embedding (e.g. a spatial covariance matrix)containing more information than a one-dimensional DOA. For example, aspatial embedding can include information pertaining to distance andreflections of sound from nearby objects.

X. Computing Environments

FIG. 14 illustrates a generalized example of a suitable computingenvironment 70 in which described technologies relating, for example, tosound learning and detection can be implemented. The computingenvironment 70 is not intended to suggest any limitation as to scope ofuse or functionality of the technologies disclosed herein, as eachtechnology may be implemented in diverse general-purpose orspecial-purpose computing environments, including within an audioappliance. For example, each disclosed technology may be implementedwith other computer system configurations, including wearable and/orhandheld appliances (e.g., a mobile-communications device, such as, forexample, IPHONE®/IPAD®/AIRPODS®/HOMEPOD™ devices, available from AppleInc. of Cupertino, Calif.), multiprocessor systems, microprocessor-basedor programmable consumer electronics, embedded platforms, networkcomputers, minicomputers, mainframe computers, smartphones, tabletcomputers, data centers, audio appliances, and the like. Each disclosedtechnology may also be practiced in distributed computing environmentswhere tasks are performed by remote processing devices that are linkedthrough a communications connection or network. In a distributedcomputing environment, program modules may be located in both local andremote memory storage devices.

As used herein, a module, or functional component, may be a programmedgeneral-purpose computer, or may be software instructions, hardwareinstructions, or both, that are executable by one or more processingunits to perform the operations described herein.

The computing environment 70 includes at least one central processingunit 71 and a memory 72. In FIG. 14 , this most basic configuration 73is included within a dashed line. The central processing unit 71executes computer-executable instructions and may be a real or a virtualprocessor. In a multi-processing system, or in a multi-core centralprocessing unit, multiple processing units execute computer-executableinstructions (e.g., threads) to increase processing speed and as such,multiple processors can run simultaneously, despite the processing unit71 being represented by a single functional block.

A processing unit, or processor, can include an application specificintegrated circuit (ASIC), a general-purpose microprocessor, afield-programmable gate array (FPGA), a digital signal controller, or aset of hardware logic structures (e.g., filters, arithmetic logic units,and dedicated state machines) arranged to process instructions.

The memory 72 may be volatile memory (e.g., registers, cache, RAM),non-volatile memory (e.g., ROM, EEPROM, flash memory, etc.), or somecombination of the two. The memory 72 stores instructions for software78 a that can, for example, implement one or more of the technologiesdescribed herein, when executed by a processor. Disclosed technologiescan be embodied in software, firmware or hardware (e.g., an ASIC).

A computing environment may have additional features. For example, thecomputing environment 70 includes storage 74, one or more input devices75, one or more output devices 76, and one or more communicationconnections 77. An interconnection mechanism (not shown) such as a bus,a controller, or a network, can interconnect the components of thecomputing environment 70. Typically, operating system software (notshown) provides an operating environment for other software executing inthe computing environment 70, and coordinates activities of thecomponents of the computing environment 70.

The store 74 may be removable or non-removable and can include selectedforms of machine-readable media. In general, machine-readable mediaincludes magnetic disks, magnetic tapes or cassettes, non-volatilesolid-state memory, CD-ROMs, CD-RWs, DVDs, magnetic tape, optical datastorage devices, and carrier waves, or any other machine-readable mediumwhich can be used to store information, and which can be accessed withinthe computing environment 70. The storage 74 can store instructions forthe software 78 b that can, for example, implement technologiesdescribed herein, when executed by a processor.

The store 74 can also be distributed, e.g., over a network so thatsoftware instructions are stored and executed in a distributed fashion.In other embodiments, e.g., in which the store 74, or a portion thereof,is embodied as an arrangement of hardwired logic structures, some (orall) of these operations can be performed by specific hardwarecomponents that contain the hardwired logic structures. The store 74 canfurther be distributed, as between or among machine-readable media andselected arrangements of hardwired logic structures. Processingoperations disclosed herein can be performed by any combination ofprogrammed data processing components and hardwired circuit, or logic,components.

The input device(s) 75 may be any one or more of the following: a touchinput device, such as a keyboard, keypad, mouse, pen, touchscreen, touchpad, or trackball; a voice input device, such as one or more microphonetransducers, speech-recognition technologies and processors, andcombinations thereof; a scanning device; or another device, thatprovides input to the computing environment 70. For audio, the inputdevice(s) 75 may include a microphone or other transducer (e.g., a soundcard or similar device that accepts audio input in analog or digitalform), or a computer-readable media reader that provides audio samplesand/or machine-readable transcriptions thereof to the computingenvironment 70.

Speech-recognition technologies that serve as an input device caninclude any of a variety of signal conditioners and controllers, and canbe implemented in software, firmware, or hardware. Further, thespeech-recognition technologies can be implemented in a plurality offunctional modules. The functional modules, in turn, can be implementedwithin a single computing environment and/or distributed between oramong a plurality of networked computing environments. Each suchnetworked computing environment can be in communication with one or moreother computing environments implementing a functional module of thespeech-recognition technologies by way of a communication connection.

The output device(s) 76 may be any one or more of a display, printer,loudspeaker transducer, DVD-writer, signal transmitter, or anotherdevice that provides output from the computing environment 70. An outputdevice can include or be embodied as a communication connection 77.

The communication connection(s) 77 enable communication over or througha communication medium (e.g., a connecting network) to another computingentity. A communication connection can include a transmitter and areceiver suitable for communicating over a local area network (LAN), awide area network (WAN) connection, or both. LAN and WAN connections canbe facilitated by a wired connection or a wireless connection. If a LANor a WAN connection is wireless, the communication connection caninclude one or more antennas or antenna arrays. The communication mediumconveys information such as computer-executable instructions, compressedgraphics information, processed signal information (including processedaudio signals), or other data in a modulated data signal. Examples ofcommunication media for so-called wired connections include fiber-opticcables and copper wires. Communication media for wireless communicationscan include electromagnetic radiation within one or more selectedfrequency bands.

Machine-readable media are any available media that can be accessedwithin a computing environment 70. By way of example, and notlimitation, with the computing environment 70, machine-readable mediainclude memory 72, storage 74, communication media (not shown), andcombinations of any of the above. As used herein, the phrase “tangiblemachine-readable” (or “tangible computer-readable”) media excludestransitory signals.

As explained above, some disclosed principles can be embodied in a store74. Such a store can include tangible, non-transitory machine-readablemedium (such as microelectronic memory) having stored thereon or thereininstructions. The instructions can program one or more data processingcomponents (generically referred to here as a “processor”) to performone or more processing operations described herein, includingestimating, computing, calculating, measuring, detecting, adjusting,sensing, measuring, filtering, correlating, and decision making, as wellas, by way of example, addition, subtraction, inversion, and comparison.In some embodiments, some or all of these operations (of a machineprocess) can be performed by specific electronic hardware componentsthat contain hardwired logic (e.g., dedicated digital filter blocks).Those operations can alternatively be performed by any combination ofprogrammed data processing components and fixed, or hardwired, circuitcomponents.

XI. Other Exemplary Embodiments

As described above, one aspect of the present technology is thegathering and use of data available from various sources to improve thedelivery to users of contextual information or any other informationthat may be of interest to them. The present disclosure contemplatesthat in some instances, this gathered data may include personalinformation data that uniquely identifies devices in a user'senvironment or can be used to contact or locate a specific person. Suchpersonal information data can include demographic data, location-baseddata, telephone numbers, email addresses, twitter ID's, home addresses,data or records relating to a user's health or level of fitness (e.g.,vital signs measurements, medication information, exercise information),date of birth, or any other identifying or personal information.

The present disclosure recognizes that the use of such personalinformation data, in the present technology, can be used to the benefitof users. For example, the personal information data can be used toissue a perceptible alert to a user in the presence of a sound, or othersignal, that the user might not perceive. Accordingly, use of suchpersonal information data enables some users to overcome a sensoryimpairment. Further, other uses for personal information data thatbenefit the user are also contemplated by the present disclosure. Forinstance, health and fitness data may be used to provide insights into auser's general wellness, or may be used as positive feedback toindividuals using technology to pursue wellness goals.

The present disclosure contemplates that the entities responsible forthe collection, analysis, disclosure, transfer, storage, or other use ofsuch personal information data will comply with well-established privacypolicies and/or privacy practices. In particular, such entities shouldimplement and consistently use privacy policies and practices that aregenerally recognized as meeting or exceeding industry or governmentalrequirements for maintaining personal information data private andsecure. Such policies should be easily accessible by users, and shouldbe updated as the collection and/or use of data changes. Personalinformation from users should be collected for legitimate and reasonableuses of the entity and not shared or sold outside of those legitimateuses. Further, such collection/sharing should occur after receiving theinformed consent of the users. Additionally, such entities shouldconsider taking any needed steps for safeguarding and securing access tosuch personal information data and ensuring that others with access tothe personal information data adhere to their privacy policies andprocedures. Further, such entities can subject themselves to evaluationby third parties to certify their adherence to widely accepted privacypolicies and practices. In addition, policies and practices should beadapted for the particular types of personal information data beingcollected and/or accessed and adapted to applicable laws and standards,including jurisdiction-specific considerations. For instance, in the US,collection of or access to certain health data may be governed byfederal and/or state laws, such as the Health Insurance Portability andAccountability Act (HIPAA); whereas health data in other countries maybe subject to other regulations and policies and should be handledaccordingly. Hence different privacy practices should be maintained fordifferent personal data types in each country.

Despite the foregoing, the present disclosure also contemplatesembodiments in which users selectively block the use of, or access to,personal information data. That is, the present disclosure contemplatesthat hardware and/or software elements can be provided to prevent orblock access to such personal information data. For example, in the caseof devices that can detect or learn to identify new sounds, the presenttechnology can be configured to allow users to select to “opt in” or“opt out” of participation in the collection of personal informationdata during registration for services or anytime thereafter. In anotherexample, users can elect not to provide examples of sounds emitted byparticular devices. In yet another example, users can elect to limit thetypes of devices to detect or learn, or entirely prohibit the detectionor learning of any devices. In addition to providing “opt in” and “optout” options, the present disclosure contemplates providingnotifications relating to the access or use of personal information. Forinstance, a user may be notified upon downloading an app that theirpersonal information data will be accessed and then reminded again justbefore personal information data is accessed by the app.

Moreover, it is the intent of the present disclosure that personalinformation data should be managed and handled in a way to minimizerisks of unintentional or unauthorized access or use. Risk can beminimized by limiting the collection of data and deleting data once itis no longer needed. In addition, and when applicable, including incertain health related applications, data de-identification can be usedto protect a user's privacy. De-identification may be facilitated, whenappropriate, by removing specific identifiers (e.g., date of birth,etc.), controlling the amount or specificity of data stored (e.g.,collecting location data a city level rather than at an address level),controlling how data is stored (e.g., aggregating data across users),and/or other methods.

Therefore, although the present disclosure broadly covers use ofpersonal information data to implement one or more various disclosedembodiments, the present disclosure also contemplates that the variousembodiments can also be implemented without the need for accessing suchpersonal information data. That is, the various embodiments of thepresent technology are not rendered inoperable due to the lack of all ora portion of such personal information data. For example,machine-detectable, environmental signals other than sound can beobserved and used to learn or detect an output from a legacy device, andsuch signals can be based on non-personal information data or a bareminimum amount of personal information, such as spectral content ofmechanical vibrations (e.g., from a person knocking on a door) observedby a device associated with a user, other non-personal informationavailable to the device (e.g., spectral content emitted by certain typesof devices, e.g., doorbells, smoke detectors, commonly found in a user'slistening environment), or publicly available information.

The examples described above generally concern classifying acousticscenes and identifying acoustic sources therein, and related systems andmethods. The previous description is provided to enable a person skilledin the art to make or use the disclosed principles. Embodiments otherthan those described above in detail are contemplated based on theprinciples disclosed herein, together with any attendant changes inconfigurations of the respective apparatus or changes in order of methodacts described herein, without departing from the spirit or scope ofthis disclosure. Various modifications to the examples described hereinwill be readily apparent to those skilled in the art.

For example, the foregoing description of selected principles aregrouped by section. Nonetheless, it shall be understood that eachprinciple (or all or no principles) in a given section can be combinedwith one or more other principles, e.g., described in another section toachieve a desired outcome or result as described herein. Suchcombinations are expressly contemplated and described by thisdisclosure, despite that every possible combination and permutation ofdisclosed principles is not listed in the interest of succinctness.

Directions and other relative references (e.g., up, down, top, bottom,left, right, rearward, forward, etc.) may be used to facilitatediscussion of the drawings and principles herein, but are not intendedto be limiting. For example, certain terms may be used such as “up,”“down,”, “upper,” “lower,” “horizontal,” “vertical,” “left,” “right,”and the like. Such terms are used, where applicable, to provide someclarity of description when dealing with relative relationships,particularly with respect to the illustrated embodiments. Such terms arenot, however, intended to imply absolute relationships, positions,and/or orientations. For example, with respect to an object, an “upper”surface can become a “lower” surface simply by turning the object over.Nevertheless, it is still the same surface and the object remains thesame. As used herein, “and/or” means “and” or “or”, as well as “and” and“or.” Moreover, all patent and non-patent literature cited herein ishereby incorporated by reference in its entirety for all purposes.

And, those of ordinary skill in the art will appreciate that theexemplary embodiments disclosed herein can be adapted to variousconfigurations and/or uses without departing from the disclosedprinciples. Applying the principles disclosed herein, it is possible toprovide a wide variety of approaches and systems for detecting targetsounds in an acoustic scene. For example, the principles described abovein connection with any particular example can be combined with theprinciples described in connection with another example describedherein.

All structural and functional equivalents to the features and methodacts of the various embodiments described throughout the disclosure thatare known or later come to be known to those of ordinary skill in theart are intended to be encompassed by the principles described and thefeatures and acts claimed herein.

Accordingly, neither the claims nor this detailed description shall beconstrued in a limiting sense, and following a review of thisdisclosure, those of ordinary skill in the art will appreciate the widevariety of methods and systems that can be devised under disclosed andclaimed concepts.

Moreover, nothing disclosed herein is intended to be dedicated to thepublic regardless of whether such disclosure is explicitly recited inthe claims. To aid the Patent Office and any readers of any patentissued on this application in interpreting the claims appended hereto orotherwise presented throughout prosecution of this or any continuingpatent application, applicants wish to note that they do not intend anyclaimed feature to be construed under or otherwise to invoke theprovisions of 35 USC 112(f), unless the phrase “means for” or “step for”is explicitly used in the particular claim.

The appended claims are not intended to be limited to the embodimentsshown herein, but are to be accorded the full scope consistent with thelanguage of the claims, wherein reference to a feature in the singular,such as by use of the article “a” or “an” is not intended to mean “oneand only one” unless specifically so stated, but rather “one or more”.

Thus, in view of the many possible embodiments to which the disclosedprinciples can be applied, we reserve the right to claim any and allcombinations of features and acts described herein, including the rightto claim all that comes within the scope and spirit of the foregoingdescription, as well as the combinations recited, literally andequivalently, in any claims presented anytime throughout prosecution ofthis application or any application claiming benefit of or priority fromthis application.

What is claimed is:
 1. An integrated circuit comprising circuitryconfigured to: automatically detect a recurrent first sound in anenvironment as detected via a microphone of a device; afterautomatically detecting the recurrent first sound, generate a promptwhether the first recurrent sound should be learned; in response toreceiving an affirmative response that the recurrent first sound shouldbe learned, generate a reference representation of the recurrent firstsound; store the reference representation of the recurrent first soundon the device; detect a second sound in the environment; and in responseto identifying the detected second sound as the recurrent first soundbased on the stored reference representation, update the storedreference representation of the recurrent first sound based on thedetected second sound.
 2. The integrated circuit of claim 1, wherein thereference representation is stored in response to the recurrent firstsound recurring a threshold number of occurrences as detected via themicrophone.
 3. The integrated circuit of claim 1, wherein the circuitryis further configured to automatically detect the recurrent first soundwithout receiving an input.
 4. The integrated circuit of claim 1,wherein the circuitry is further configured to, based on the detectedsecond sound being within a threshold difference of the stored referencerepresentation, provide an output.
 5. The integrated circuit of claim 1,wherein the circuitry is further configured to provide an output inresponse to the detected second sound being detected via the microphone.6. The integrated circuit of claim 1, wherein: the circuitry is furtherconfigured to assign the detected second sound to a selectedclassification, and provide an output corresponding to the selectedclassification.
 7. The integrated circuit of claim 1, wherein: thecircuitry is further configured to determine whether a class of sound ispresent in the detected second sound, and provide an output in responseto a determination the class of sound is present in the detected secondsound.
 8. The integrated circuit of claim 1, wherein the circuitry isfurther to transmit an output signal to another device in response toidentifying the detected second sound as the recurrent first sound. 9.The integrated circuit of claim 1, wherein the circuitry is furtherconfigured to obtain authorization, based on the affirmative response tothe prompt, to generate the reference representation.
 10. The integratedcircuit of claim 1, wherein the detected second sound comprises acombination of the recurrent first sound and an impairment.
 11. Anelectronic device, comprising: a microphone; and a controller comprisingcircuitry, the circuity configured to: detect, using the microphone, afirst sound from a device; in response to detecting the first sound,provide a prompt whether the first sound should be learned; generate,based on an affirmative response to the prompt, a referencerepresentation of the first sound; detect, using the microphone, asecond sound; compare the second sound with the reference representationof the first sound; and in response to the second sound being within athreshold difference of the reference representation of the first sound,provide an output indicating a known sound is detected.
 12. Theelectronic device of claim 11, wherein the known sound corresponds apresence of the first sound.
 13. The electronic device of claim 11,wherein the first sound comprises a recurrent sound.
 14. The electronicdevice of claim 13, wherein the circuitry is configured to automaticallydetect the recurrent sound without an input.
 15. The electronic deviceof claim 11, wherein the second sound comprises an impaired version ofthe first sound.
 16. The electronic device of claim 15, wherein theimpaired version comprises the first sound and noise.
 17. A method formanaging incoming audio signals, the method comprising: automaticallyobtaining, by a microphone, a first recurrent sound from a device;subsequent to the first recurrent sound recurring a threshold number ofoccurrences, generating a reference representation of the firstrecurrent sound; detecting, by the microphone, a second sound from thedevice; in response to the second sound being within a thresholddifference of the reference representation of the first recurrent sound,updating the reference representation; and storing the updated referencerepresentation.
 18. The method of claim 17, further comprising:providing a prompt whether the first recurrent sound should be learned;and in response to an affirmative response to the prompt, generating thereference representation.
 19. The method of claim 18, further comprisinggenerating, based on a comparison between the reference representationand the second sound, the prompt.
 20. The method of claim 17, furthercomprising providing an output indicating a known sound is detected.